Seismic data fusion method and device and computing equipment

Abstract

The invention discloses a seismic data fusion method and device and computing equipment, and the method comprises the steps: comparing the spectral analysis results of towing cable seismic data and seabed node seismic data, and determining a fusion frequency band; respectively extracting data in a target fusion frequency band in the streamer seismic data and the seabed node seismic data, calculating index difference data of various seismic attributes according to the extracted first seismic data and second seismic data, and further determining weight data of various seismic attributes; calculating first weighted data of the first seismic data according to the weight data of various seismic attributes, and calculating second weighted data of the second seismic data according to the first weighted data; and performing weighted fusion according to the first seismic data, the first weighted data, the second seismic data and the second weighted data to obtain fused seismic data of the target fusion frequency band. Through the above mode, the advantages of the seismic data under different frequency bands and different attributes can be fully utilized, and the fusion effect of the seismic data can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of seismic data analysis, and in particular to a seismic data fusion method, device, computing equipment, computer storage medium and computer program product. Background Art

[0002] With the continuous deepening of the marine exploration process, the requirements for secondary three-dimensional seabed node seismic acquisition for target exploration are becoming higher and higher, and the difficulty is increasing. The imaging of seabed node seismic data not only needs to take into account the needs of exploration and development at the same time, but also needs to reduce the exploration cost. Therefore, it is strongly required to further tap the potential of new and old data to provide greater geophysical value for exploration and development. Since the geological targets faced by seabed node seismic data are completely different, their acquisition methods and parameters are very different, which directly leads to huge differences in frequency, phase, amplitude, signal-to-noise ratio and even formation occurrence between conventional towed cable acquisition and seabed node acquisition. If the data of different periods are directly superimposed, it is difficult to give full play to the advantages of the two, and it may even destroy the reliability of seismic imaging, thereby causing geological illusions.

[0003] In recent years, image fusion processing technology has developed rapidly, and it is mainly used in the fields of military, medicine, and aviation satellite image processing. There are few studies on fusion technology for offshore seismic data processing. Common fusion methods include fusion processing technology based on signal-to-noise ratio consistency and fusion processing technology based on wavelet transform. Fusion processing technology based on signal-to-noise ratio consistency uses the signal-to-noise ratio of different seismic data as a reference to select imaging areas with high signal-to-noise ratio for image fusion. Fusion processing technology based on wavelet transform uses wavelet transform to subdivide seismic data into multiple scales, and selects the image with the largest average amplitude at different scales for image fusion. However, existing technologies have encountered huge challenges in the fusion processing of multi-period seismic data with large differences. Both methods use a single attribute of seismic data for fusion, and it is difficult to maximize the complementarity of the advantages and disadvantages of the towline and seabed node seismic data in many aspects, so it is impossible to obtain the best fusion imaging effect. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide a seismic data fusion method, apparatus, computing device, computer storage medium and computer program product that overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of the present application, a seismic data fusion method is provided, comprising:

[0006] Conduct spectrum analysis on the streamer seismic data and the seafloor node seismic data respectively, and compare the spectrum analysis results to determine each fusion frequency band;

[0007] Extracting data in a target fusion frequency band from the streamer seismic data and the seafloor node seismic data respectively to obtain first seismic data and second seismic data; calculating index gap data of various seismic attributes based on the first seismic data and the second seismic data; the target fusion frequency band is any fusion frequency band among the fusion frequency bands;

[0008] According to the index gap data of various seismic attributes, the weight data of various seismic attributes are determined;

[0009] Calculating first weighted data of the first seismic data according to weight data of various seismic attributes, and calculating second weighted data of the second seismic data according to the first weighted data;

[0010] A weighted fusion process is performed on the first seismic data and its first weighted data, and the second seismic data and its second weighted data to obtain fused seismic data of a target fused frequency band.

[0011] Optionally, the method further comprises:

[0012] Acquire seafloor node seismic data and raw streamer seismic data;

[0013] Based on the seafloor node seismic data, the original streamer seismic data is phase-registered to obtain the streamer seismic data.

[0014] Optionally, the index gap data of each seismic attribute includes the index difference value of the seismic attribute corresponding to each imaging point, and the weight data of each seismic attribute includes the weight coefficient of the seismic attribute corresponding to each imaging point;

[0015] Determining weight data of various seismic attributes based on the indicator gap data of various seismic attributes further includes:

[0016] For the index difference data of each seismic attribute, the index difference values ​​of each imaging point contained therein are normalized to obtain the weight coefficient of the seismic attribute corresponding to each imaging point.

[0017] Optionally, the first weighted data of the first seismic data includes a first weighting coefficient of each imaging point;

[0018] Calculating first weighted data of the first seismic data according to the weight data of various seismic attributes further comprises:

[0019] The weight coefficients of various seismic attributes corresponding to each imaging point are added in equal proportion to obtain the first weight coefficient of the imaging point.

[0020] Optionally, the various seismic attributes include at least two of resolution attributes, imaging quality attributes, and structural attributes.

[0021] Optionally, calculating the indicator gap data of various seismic attributes according to the first seismic data of the streamer seismic data within the target fusion frequency band and the second seismic data of the seafloor node seismic data within the target fusion frequency band further comprises:

[0022] Calculating a first instantaneous frequency of each imaging point according to the first seismic data, and calculating a second instantaneous frequency of each imaging point according to the second seismic data;

[0023] The first instantaneous frequency and the second instantaneous frequency of each imaging point are subtracted to obtain the index difference value of the resolution attribute corresponding to each imaging point.

[0024] Optionally, calculating the indicator gap data of various seismic attributes according to the first seismic data of the streamer seismic data within the target fusion frequency band and the second seismic data of the seafloor node seismic data within the target fusion frequency band further comprises:

[0025] Calculating a first signal-to-noise ratio of each imaging point according to the first seismic data, and calculating a second signal-to-noise ratio of each imaging point according to the second seismic data;

[0026] The first signal-to-noise ratio and the second signal-to-noise ratio of each imaging point are subtracted to obtain the index difference of the imaging quality attribute corresponding to each imaging point.

[0027] Optionally, calculating the indicator gap data of various seismic attributes according to the first seismic data of the streamer seismic data within the target fusion frequency band and the second seismic data of the seafloor node seismic data within the target fusion frequency band further comprises:

[0028] Calculating a first dip angle of each imaging point according to the first seismic data, and calculating a second dip angle of each imaging point according to the second seismic data;

[0029] The first inclination angle and the second inclination angle of each imaging point are subtracted to obtain the index difference of the structural attribute corresponding to each imaging point.

[0030] According to another aspect of the present application, a seismic data fusion device is provided, comprising:

[0031] The frequency band division module is used to perform spectrum analysis on the streamer seismic data and the seafloor node seismic data respectively, and compare the spectrum analysis results to determine each fusion frequency band;

[0032] The difference processing module is used to extract the data in the target fusion frequency band from the streamer seismic data and the seafloor node seismic data respectively to obtain the first seismic data and the second seismic data; calculate the index difference data of various seismic attributes according to the first seismic data and the second seismic data; the target fusion frequency band is any fusion frequency band among the fusion frequency bands;

[0033] A weight processing module, for determining weight data of various seismic attributes according to the index gap data of various seismic attributes; calculating first weighted data of first seismic data according to the weight data of various seismic attributes, and calculating second weighted data of second seismic data according to the first weighted data;

[0034] The fusion module is used to perform weighted fusion processing on the first seismic data and its first weighted data, the second seismic data and its second weighted data, so as to obtain fused seismic data of a target fusion frequency band.

[0035] Optionally, the device further comprises:

[0036] The registration module is used to obtain the seabed node seismic data and the original streamer seismic data; based on the seabed node seismic data, the original streamer seismic data is subjected to phase registration processing to obtain the streamer seismic data.

[0037] Optionally, the index gap data of each seismic attribute includes the index difference value of the seismic attribute corresponding to each imaging point, and the weight data of each seismic attribute includes the weight coefficient of the seismic attribute corresponding to each imaging point;

[0038] The weight processing module is further used to:

[0039] For the index difference data of each seismic attribute, the index difference values ​​of each imaging point contained therein are normalized to obtain the weight coefficient of the seismic attribute corresponding to each imaging point.

[0040] Optionally, the first weighted data of the first seismic data includes a first weighting coefficient of each imaging point;

[0041] The weight processing module is further used to:

[0042] The weight coefficients of various seismic attributes corresponding to each imaging point are added in equal proportion to obtain the first weight coefficient of the imaging point.

[0043] Optionally, the various seismic attributes include at least two of resolution attributes, imaging quality attributes, and structural attributes.

[0044] Optionally, the difference processing module is further used for:

[0045] Calculating a first instantaneous frequency of each imaging point according to the first seismic data, and calculating a second instantaneous frequency of each imaging point according to the second seismic data;

[0046] The first instantaneous frequency and the second instantaneous frequency of each imaging point are subtracted to obtain the index difference value of the resolution attribute corresponding to each imaging point.

[0047] Optionally, the difference processing module is further used for:

[0048] Calculating a first signal-to-noise ratio of each imaging point according to the first seismic data, and calculating a second signal-to-noise ratio of each imaging point according to the second seismic data;

[0049] The first signal-to-noise ratio and the second signal-to-noise ratio of each imaging point are subtracted to obtain the index difference of the imaging quality attribute corresponding to each imaging point.

[0050] Optionally, the difference processing module is further used for:

[0051] Calculating a first dip angle of each imaging point according to the first seismic data, and calculating a second dip angle of each imaging point according to the second seismic data;

[0052] The first inclination angle and the second inclination angle of each imaging point are subtracted to obtain the index difference of the structural attribute corresponding to each imaging point.

[0053] According to another aspect of the present application, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0054] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned seismic data fusion method.

[0055] According to another aspect of the present application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned seismic data fusion method.

[0056] According to another aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned seismic data fusion method.

[0057] According to the seismic data fusion method, device, computing equipment, computer storage medium and computer program product provided in the present application, spectrum analysis is performed on the streamer seismic data and the seabed node seismic data respectively, and each fusion frequency band is determined by comparing the spectrum analysis results; the data in the target fusion frequency band in the streamer seismic data and the seabed node seismic data are extracted respectively to obtain the first seismic data and the second seismic data; the index gap data of various seismic attributes are calculated based on the first seismic data and the second seismic data; the target fusion frequency band is any fusion frequency band in each fusion frequency band; the weight data of various seismic attributes are determined based on the index gap data of various seismic attributes; the first weighted data of the first seismic data is calculated based on the weight data of various seismic attributes, and the second weighted data of the second seismic data is calculated based on the first weighted data; the weighted fusion processing is performed based on the first seismic data and its first weighted data, and the second seismic data and its second weighted data to obtain the fused seismic data of the target fusion frequency band. Through the above method, the fusion frequency band is divided according to the imaging difference between the streamer seismic data and the seabed node seismic data, the seismic data is fused according to the fusion frequency band, and the advantages of different seismic data are identified by using multiple seismic attributes to obtain the dynamic weighting coefficient in the frequency domain. The advantages of seismic data in different frequency bands and different attributes can be fully utilized, so as to maximize the imaging advantages of the streamer seismic data and the seabed node seismic data, and improve the seismic data fusion effect.

[0058] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0060] Figure 1 A flow chart of a seismic data fusion method provided by an embodiment of the present application is shown;

[0061] Figure 2 A flow chart of a seismic data fusion method provided by another embodiment of the present application is shown;

[0062] Figure 3 An imaging cross-section of seismic data of an ocean bottom node is shown;

[0063] Figure 4An imaging cross section of raw streamer seismic data is shown;

[0064] Figure 5 An imaging profile of streamer seismic data after phase registration is shown;

[0065] Figure 6 Another imaging cross section of streamer seismic data is shown;

[0066] Figure 7 An imaging cross-section of seismic data of another seafloor node is shown;

[0067] Figure 8 A schematic diagram showing the spectrum analysis results of layer system 1;

[0068] Fig. 9 A schematic diagram showing the spectrum analysis results of layer 2;

[0069] Fig.10 A schematic diagram showing the spectrum analysis results of layer 3;

[0070] Fig.11 A schematic diagram showing the spectrum analysis results of layer 4;

[0071] Fig.12 A schematic diagram showing the comparison of spectrum analysis results is shown;

[0072] Fig.13 A cross-sectional diagram showing weight data associated with instantaneous frequency for first seismic data corresponding to a frequency band of 0-16 Hz;

[0073] Fig.14 A cross-sectional diagram of weight data related to the dip angle of the first seismic data corresponding to the 0-16 Hz frequency band is shown;

[0074] Fig.15 A cross-sectional diagram showing weight data related to the signal-to-noise ratio of the first seismic data corresponding to the 0-16 Hz frequency band;

[0075] Fig.16 A cross-sectional diagram showing multi-attribute constraint weighting coefficients of second seismic data corresponding to a frequency band of 0-16 Hz;

[0076] Fig.17 A cross-sectional diagram of multi-attribute constraint weighting coefficients of the first seismic data corresponding to a frequency band of 0-16 Hz is shown;

[0077] Fig.18 An imaging profile of the streamer seismic data before fusion is shown;

[0078] Fig.19 An imaging cross section of the seafloor node seismic data before fusion is shown;

[0079] Fig. 20 An imaging cross section of fused seismic data is shown;

[0080] Fig.21 A functional structure diagram of a seismic data fusion device provided in an embodiment of the present application is shown;

[0081] Fig. 22 A schematic diagram of the structure of a computing device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0082] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0083] First, the terms involved in one or more embodiments of the present application are explained.

[0084] Streamer seismic data and seafloor node seismic data are two different data bodies obtained from marine seismic exploration technologies. Streamer seismic data refers to data collected by towing a long cable (called a streamer or streamline) from behind a moving ship while emitting sound waves underwater and recording the reflected signals. The streamer is equipped with multiple geophones to capture seismic waves. Seafloor node seismic data refers to data obtained by laying independent seafloor nodes (Ocean Bottom Node, referred to as OBN) on the seafloor to record seismic wave reflection information.

[0085] In an actual application scenario, the exploration target sea area is a shallow water area with a relatively flat seabed and an average water depth of about 80m. The target of early exploration is shallow gas, and the core of the secondary seabed node acquisition is the resolution of shallow strata. During the first construction, the air gun source towed at the stern continuously excited seismic waves, and the towed cable was used to receive seismic signals. During the seismic acquisition process, the source was placed at a depth of 3 meters, the cable was placed at a depth of 4 meters, and the cable length was 3,000 meters. After 20 years of oil and gas exploration, the focus has shifted to the middle and deep layers. However, there are large fuzzy areas in the middle and deep layers, and the quality of seismic imaging is very poor. The towed cable data collected in the early stage cannot meet the requirements of medium and deep oil and gas exploration even after heavy processing. Therefore, a secondary three-dimensional seismic exploration was carried out in this sea area, and the inherent advantages of seabed node acquisition such as "rich low frequency, high coverage, and long offset" were used to overcome the formation imaging in the middle and deep fuzzy areas. The two acquisitions have different targets and observation methods. The towed cable data has richer high frequencies in the shallow and medium-deep non-ambiguous areas, while the seabed node seismic data has richer low frequencies and higher signal-to-noise ratios in the medium-deep fuzzy areas. Therefore, the acquired data have their own advantages. In order to further strengthen the three-dimensional exploration and development process in this sea area, it is necessary to fuse the seismic data acquired twice to obtain higher quality seismic data to meet the needs of geological interpretation.

[0086] Figure 1 FIG. 1 is a flow chart of a seismic data fusion method provided by an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0087] Step S110, performing spectrum analysis on the streamer seismic data and the seafloor node seismic data respectively, and comparing the spectrum analysis results to determine each fusion frequency band.

[0088] The streamer seismic data specifically includes seismic data of each imaging point, and a set of channel numbers (ie, seismic channel numbers) and time information correspond to one imaging point. Similarly, the seafloor node seismic data also includes seismic data of each imaging point.

[0089] Perform spectrum analysis on the streamer seismic data to obtain the first spectrum analysis result; perform spectrum analysis on the seafloor node seismic data to obtain the second spectrum analysis result; compare the first spectrum analysis result with the second spectrum analysis result to determine the dominant frequency band with stronger energy and better imaging, and then divide multiple fusion frequency bands according to the dominant frequency band. For each endpoint frequency of each fusion frequency band, there is always an endpoint frequency of the dominant frequency band that is consistent with it, and the starting frequency and ending frequency of the frequency band are the endpoint frequencies.

[0090] Step S120, respectively extracting data within a target fusion frequency band from the streamer seismic data and the seafloor node seismic data to obtain first seismic data and second seismic data; and calculating index gap data of various seismic attributes based on the first seismic data and the second seismic data.

[0091] The data in the target fusion frequency band of the streamer seismic data is extracted to obtain the first seismic data; the data in the target fusion frequency band of the seafloor node seismic data is extracted to obtain the second seismic data. The target fusion frequency band is any fusion frequency band among the fusion frequency bands. In specific implementation, each fusion frequency band can be sequentially determined as the target fusion frequency band, and the two seismic data are fused for the target fusion frequency band.

[0092] Among them, the index difference data of the seismic attribute includes the index difference of the corresponding seismic attribute of each imaging point. Taking seismic attribute A as an example, the process of calculating the index difference data of seismic attribute A is as follows: first, the first index data of seismic attribute A corresponding to each imaging point is calculated according to the first seismic data, and the second index data of seismic attribute A corresponding to each imaging point is calculated; then, the first index data and the second index data of seismic attribute A corresponding to the same imaging point are subtracted to obtain the index difference of seismic attribute A corresponding to the imaging point, and the index difference of seismic attribute A corresponding to multiple imaging points constitutes the index difference data of seismic attribute A.

[0093] S130, determining weight data of various seismic attributes according to the index gap data of various seismic attributes.

[0094] The weight data of each seismic attribute contains the weight coefficient of the seismic attribute corresponding to each imaging point. The index difference of the seismic attribute can reflect the dominant distribution of the streamer seismic data and the seabed node seismic data on the seismic attribute, so it can be used to determine the weight data for seismic data fusion.

[0095] Step S140, calculating first weighted data of the first seismic data according to weight data of various seismic attributes, and calculating second weighted data of the second seismic data according to the first weighted data.

[0096] The first weighted data includes the first weighted coefficient corresponding to each imaging point, and the second weighted data also includes the second weighted coefficient corresponding to each imaging point. For one imaging point, the weight coefficients of various seismic attributes corresponding to the imaging point are summarized to obtain the first weighted coefficient corresponding to the imaging point. Based on the principle that the sum of the weighted coefficients is 1, the second weighted coefficient corresponding to the imaging point can be calculated.

[0097] In the method of the embodiment of the present application, the weighting coefficient is calculated based on the index difference data of various seismic attributes, so that multiple seismic attributes are used for weighted constraints, which can fully and scientifically utilize the advantages of seismic data in different dimensions.

[0098] Step S150, performing weighted fusion processing on the first seismic data and its first weighted data, the second seismic data and its second weighted data, to obtain fused seismic data of a target fused frequency band.

[0099] The weighted sum is calculated based on the first seismic data and the first weighted data, the second seismic data and the second weighted data to obtain the fused seismic data of the target fused frequency band. Specifically, for the same imaging point, the data corresponding to the imaging point in the first seismic data and the first weighted coefficient corresponding to the imaging point in the first weighted data are determined, and the data corresponding to the imaging point in the second seismic data and the second weighted coefficient corresponding to the imaging point in the second weighted data are determined, and then the weighted sum is calculated based on the determined four data to obtain the fused seismic data of the imaging point, and the fusion results of the seismic data of multiple imaging points constitute the fused seismic data of the target fused frequency band.

[0100] To summarize, according to the seismic data fusion method provided in this embodiment, the fusion frequency band is divided according to the imaging difference between the towed cable seismic data and the seabed node seismic data, the seismic data is fused according to the fusion frequency band, and the advantages of different seismic data are identified by using multiple seismic attributes to obtain dynamic weighting coefficients in the frequency domain. This can fully utilize the advantages of seismic data in different frequency bands and different attributes, thereby maximizing the use of the respective imaging advantages of the towed cable seismic data and the seabed node seismic data, and can improve the seismic data fusion effect.

[0101] Figure 2 FIG. 2 is a flow chart of a seismic data fusion method provided by another embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0102] Step S210, acquiring seafloor node seismic data and original streamer seismic data; taking the seafloor node seismic data as a reference, performing phase registration processing on the original streamer seismic data to obtain the streamer seismic data.

[0103] The geological targets targeted by the acquisition tasks of the same work area at different times are completely different. The early towline acquisition direction is north-south, while the direction of the secondary three-dimensional seafloor node acquisition is east-west, so the seismic acquisition parameters are also different, and because the wave field path difference is large, even if the same velocity model imaging is used, there is a local phase difference between the two. In order to obtain the best fusion effect, the difference caused by non-geological reasons between the towline seismic data and the seafloor node seismic data needs to be eliminated before fusion. In addition, since the underground medium is usually anisotropic, if the acquisition direction is different during acquisition, the propagation characteristics of its wave field will also be greatly different. The current geophysical processing technology is difficult to obtain accurate anisotropy parameters, resulting in differences in the occurrence of the seismic data collected by the towline and the seafloor node even if they are in the same stratum. In the method of the embodiment of the present application, the DWT (dynamic time rule) technology is used for phase registration processing to match and align seismic waveforms with large differences in stratum occurrence.

[0104] Specifically, the seismic data of the seabed node and the seismic data of the same seismic channel in the original streamer seismic data are respectively determined as the first sequence and the second sequence, and the local distance matrix is ​​obtained according to the first sequence and the second sequence, and the minimum cumulative cost matrix is ​​obtained, the minimum cumulative cost matrix is ​​backtracked, and the optimal path is determined, and the time offset is obtained according to the data correspondence corresponding to each path node in the optimal path, and the second sequence is phase-aligned according to the time offset.

[0105] Specifically, the seismic data of one channel in the seafloor node seismic data is represented as a time series X = (x1, x2, ..., x i , …, x n ), in which n represents the number of sampling points of the seafloor node seismic data in time, x i represents the seismic data of the ith sampling point, i∈[1,n]; the data of the same channel in the original streamer seismic data is represented by the time series Y=(y1, y2,…, y j , …, y m ), in which m represents the number of sampling points of the original streamer seismic data in time, and y j represents the seismic data of the jth time sampling point, j∈[1,m]. The goal of phase registration processing is to find the optimal path to align the original streamer seismic data to the seafloor node data so that the cumulative alignment cost is minimized. The optimal path W is shown in formula (1):

[0106] W=(W1,W2,…W a , …, W k ) (1);

[0107] In formula (1), the optimal path contains k path nodes, W aThat is, it corresponds to a path node in the optimal path, which represents the corresponding relationship of a set of data, W a-1 W a The predecessor node, assuming W a =(i, j), then it means the i-th data x in sequence X i and the jth data y of sequence Y j Correspondingly, according to x i The corresponding time and y j The corresponding time can determine the offset, and then y j The time is updated, that is, y is completed. j Phase alignment processing.

[0108] Figure 3 shows an imaging cross-section of seismic data from an ocean bottom node. Figure 4 shows an imaging cross section of raw streamer seismic data, Figure 5 An imaging profile of the streamer seismic data after phase registration is shown. Figure 3 , Figure 4 as well as Figure 5 The horizontal axis is the track number and the vertical axis is the time. The figure uses colors to distinguish lithology. Different colors represent the lithology differences of the strata. The color depth represents the size of the underground wave impedance. The greater the density of the underground strata, the darker the color.

[0109] Step S220, performing spectrum analysis on the streamer seismic data and the seafloor node seismic data respectively, and comparing the spectrum analysis results to determine each fusion frequency band.

[0110] The spectrum analysis of the streamer seismic data and the seafloor node seismic data in each layer (i.e., each layer at different depths) is performed respectively. According to the energy distribution of the streamer seismic data and the seafloor node seismic data in the spectrum of different layers, the dominant frequency band of each layer is determined, and then each fusion frequency band is obtained according to the dominant frequency band of each layer. For each end frequency of each fusion frequency band, there is always a consistent end frequency of the dominant frequency band, and the starting frequency and the ending frequency of the frequency band are the end frequencies.

[0111] It should be noted that the number of fusion bands should be appropriate. If there are too many fusion bands, it is easy to produce oscillation in the high frequency band. If there are too few fusion bands, it is difficult to bring out the advantages of different layers. Generally, there are 4-7 fusion bands. Reflected waves are usually composite waves with a wide frequency range of about 10-125Hz and a main frequency of about 20-90Hz.

[0112] Specifically, discrete Fourier transform is used to transform the time domain function into the frequency domain. The conversion method is shown in formula (2):

[0113]

[0114] In formula (2), x[n] represents the discrete time domain signal sequence, n is the index of the time series, and X(e jω ) represents the discrete Fourier transform result, which is the frequency domain signal, e -jωn represents a complex exponential function. The streamer seismic data and the seafloor node seismic data are respectively determined as x[n] to achieve the conversion of time domain data to frequency domain representation.

[0115] Figure 6 Another imaging cross section of streamer seismic data is shown; Figure 7 shows an imaging cross-section of seismic data from another seafloor node. Figure 6 and Figure 7 The horizontal axis represents the channel number, and the vertical axis represents the time. Discrete Fourier transform is used to perform spectrum analysis on the four main layers of the streamer seismic data and the seafloor node seismic data to obtain the energy distribution of the spectra of different layers. Figure 8 A schematic diagram showing the spectrum analysis results of layer 1, Fig. 9 A schematic diagram showing the spectrum analysis results of layer 2, Fig.10 A schematic diagram showing the spectrum analysis results of layer 3, Fig.11 A schematic diagram showing the results of spectrum analysis of layer system 4 is shown.

[0116] Fig.12 A comparison diagram of the spectrum analysis results is shown, which is drawn based on the spectrum analysis results of the four layers of the towed seismic data and the seafloor node seismic data. For layer 1, it can be seen from the spectrum analysis results that the complementary phenomenon between the towed seismic data and the seafloor node seismic data is very obvious, and the towed seismic data has obvious advantages in frequencies above 55Hz and below 85Hz. Therefore, the frequency extracted for layer 1 is 55Hz-85Hz. Similarly, the dominant frequency bands of layers 2, 3, and 4 can be extracted, which are 40Hz-75Hz, 16Hz-45Hz, and 10Hz-32Hz, respectively. In order to ensure that the fusion frequency bands are not divided too finely, five fusion frequency bands are obtained, namely: 0-16Hz, 16Hz-32Hz, 32Hz-55Hz, 55Hz-85Hz, and 85Hz-120Hz.

[0117] Step S230, extracting data in a target fusion frequency band from the streamer seismic data and the seafloor node seismic data respectively to obtain first seismic data and second seismic data; and calculating index gap data of various seismic attributes based on the first seismic data and the second seismic data.

[0118] The data in the target fusion frequency band of the streamer seismic data is extracted to obtain the first seismic data; the data in the target fusion frequency band of the seafloor node seismic data is extracted to obtain the second seismic data. The target fusion frequency band is any fusion frequency band among the fusion frequency bands. In specific implementation, each fusion frequency band can be sequentially determined as the target fusion frequency band, and the fusion processing of the two seismic data is performed for the target fusion frequency band.

[0119] Step S240 , for each type of seismic attribute index difference data, normalize the index difference values ​​of each imaging point contained therein to obtain a weight coefficient of the seismic attribute corresponding to each imaging point.

[0120] The index difference data of each seismic attribute includes the index difference value of the seismic attribute corresponding to each imaging point, and the weight data of each seismic attribute includes the weight coefficient of the seismic attribute corresponding to each imaging point.

[0121] Seismic attributes may specifically include resolution attributes. The resolution attribute is based on the instantaneous frequency of seismic data. The higher the instantaneous frequency, the higher the resolution. The weight coefficient related to the resolution can be obtained by normalizing the instantaneous frequency difference between the towed seismic data and the seafloor node seismic data. Specifically, first, the first instantaneous frequency of each imaging point is calculated based on the first seismic data, and the second instantaneous frequency of each imaging point is calculated based on the second seismic data; the first instantaneous frequency and the second instantaneous frequency of each imaging point are subtracted to obtain the index difference of the resolution attribute corresponding to each imaging point; then, the index difference of the resolution attribute corresponding to the imaging point is normalized to obtain the weight coefficient of the resolution attribute corresponding to the imaging point. The specific calculation process is as follows:

[0122] First, the Hilbert transform is used to extract the instantaneous frequency. The calculation method is shown in formula (3):

[0123]

[0124] In formula (3), f(t,x,y) represents the instantaneous frequency, represents the instantaneous phase, t represents time, u represents the input data, u is the frequency domain representation corresponding to the time domain seismic data, and u H is the Hilbert transform corresponding to u, and u and u are obtained through Fourier transform H The partial differential of , when specifically calculating, u is determined according to the first seismic data and the second seismic data respectively, so as to obtain the first instantaneous frequency and the second instantaneous frequency.

[0125] For the first seismic data, the first instantaneous frequency of each imaging point is determined in the above manner. Similarly, for the second seismic data, the second instantaneous frequency of each imaging point is determined in the above manner. Then, the first instantaneous frequency and the second instantaneous frequency of the same imaging point are subtracted to obtain the distribution coefficient of the spatial time-varying dominant frequency of the data. The calculation method is shown in formula (4):

[0126] Δf=f1-f2 (4);

[0127] In formula (4), Δf represents the difference between the first instantaneous frequency and the second instantaneous frequency of an imaging point, that is, the index difference of the resolution attribute, f1 represents the first instantaneous frequency calculated based on the streamer seismic data, and f2 represents the second instantaneous frequency calculated based on the seafloor node seismic data.

[0128] After that, the index difference of the resolution attribute corresponding to the imaging point is normalized to obtain the weight coefficient of the resolution attribute corresponding to the imaging point. The calculation method is shown in formula (5):

[0129]

[0130] In formula (5), Δf min The smallest index difference in the index gap data representing the resolution attribute, Δf max The maximum index difference in the index difference data representing the resolution attribute, w 1f Represents the weight coefficient of the resolution attribute corresponding to the imaging point.

[0131] The above-mentioned method is used to calculate each imaging point, and the weight data of the resolution attribute of the target fusion band can be obtained, which includes the weight coefficients of the resolution attributes corresponding to multiple imaging points, that is, the weight coefficients of the streamer seismic data related to the resolution in the target fusion band.

[0132] Seismic attributes may also specifically include imaging quality attributes. Imaging quality attributes are mainly based on the signal-to-noise ratio of seismic data. The higher the signal-to-noise ratio, the better the seismic imaging quality. The signal-to-noise ratio difference between the towed cable seismic data and the seafloor node seismic data is normalized to obtain the weight coefficient related to the imaging quality attribute. Specifically, first, the first signal-to-noise ratio of each imaging point is calculated based on the first seismic data, and the second signal-to-noise ratio of each imaging point is calculated based on the second seismic data; the first signal-to-noise ratio and the second signal-to-noise ratio of each imaging point are subtracted to obtain the index difference of the imaging quality attribute corresponding to each imaging point; then, the index difference of the imaging quality attribute corresponding to the imaging point is normalized to obtain the weight coefficient of the imaging quality attribute corresponding to the imaging point. The specific calculation process is as follows:

[0133] First, the signal-to-noise ratio is calculated using the principle of seismic signal coherence, as shown in formula (6):

[0134]

[0135] In formula (6), S represents the signal-to-noise ratio, d ij The data volume representing the seismic data time window, E S Indicates the signal power, E N0 represents the noise power, M and N are used to represent the size of the seismic data window, M represents the total number of sampling points in the spatial direction of the seismic data window, i represents the sampling point number in the spatial direction of the seismic data window, N represents the total number of sampling points in the temporal direction of the seismic data window, and j represents the sampling point number in the temporal direction of the seismic data window. In the specific calculation, the first seismic data and the second seismic data are respectively used as d ij , so as to calculate the first signal-to-noise ratio and the second signal-to-noise ratio of the imaging point.

[0136] For the first seismic data, the first signal-to-noise ratio of each imaging point is determined in the above manner. Similarly, for the second seismic data, the second signal-to-noise ratio of each imaging point is determined in the above manner. Then, the first signal-to-noise ratio and the second signal-to-noise ratio of the same imaging point are subtracted to obtain the distribution coefficient of the signal-to-noise ratio advantage of the streamer seismic data. The calculation method is shown in formula (7):

[0137] ΔS = S1 - S2 (7);

[0138] In formula (7), ΔS represents the difference between the first signal-to-noise ratio and the second signal-to-noise ratio of the imaging point, that is, the index difference of the imaging quality attribute, S1 represents the first signal-to-noise ratio calculated based on the streamer seismic data, and S2 represents the second signal-to-noise ratio calculated based on the seafloor node seismic data.

[0139] Afterwards, the index difference of the imaging quality attribute corresponding to the imaging point is normalized to obtain the weight coefficient of the imaging quality attribute corresponding to the imaging point. The specific calculation method is shown in formula (8):

[0140]

[0141] In formula (8), Δs min The smallest index difference in the index gap data representing the imaging quality attribute, Δs max The maximum index difference value in the index gap data representing the imaging quality attribute represents the weight coefficient of the imaging quality attribute corresponding to the imaging point.

[0142] The above-mentioned method is used to calculate each imaging point, and the weight data of the imaging quality attributes of the target fusion frequency band can be obtained, which includes the weight coefficients of the imaging quality attributes corresponding to multiple imaging points, that is, the weight coefficient of the towed seismic data related to the imaging quality in the target fusion frequency band.

[0143] Seismic attributes may also specifically include structural attributes. The indicators of structural attributes are mainly based on the dip information of seismic data. The more steep dip information, the richer the details of the geological structure imaging. By normalizing the dip difference between the towed seismic data and the seabed node seismic data, a weight coefficient consistent with the geological structure can be obtained. Specifically, first, the first dip of each imaging point is calculated according to the first seismic data, and the second dip of each imaging point is calculated according to the second seismic data; the first dip and the second dip of each imaging point are subtracted to obtain the indicator difference of the structural attribute corresponding to each imaging point; then, the indicator difference of the structural attribute corresponding to the imaging point is normalized to obtain the weight coefficient of the structural attribute corresponding to the imaging point. The specific calculation process is as follows:

[0144] First, the complex seismic trace algorithm is used to obtain the inclination field. The calculation method is shown in formula (9):

[0145]

[0146] In formula (9), θ represents the inclination angle, k x represents the instantaneous wave number in the x direction, k y represents the instantaneous wave number in the y direction, k z It represents the instantaneous wave number in the z direction, the x direction represents the main survey line direction in the coordinate system of the three-dimensional data body (three-dimensional seismic data), the y direction represents the connecting survey line direction in the coordinate system of the three-dimensional data body, and the z direction represents the vertical time direction in the coordinate system of the three-dimensional data body.

[0147] For the first seismic data, the first dip angle of each imaging point is determined in the above manner. Similarly, for the second seismic data, the second dip angle of each imaging point is determined in the above manner. Then, the first dip angle and the second dip angle of the same imaging point are subtracted to obtain the distribution coefficient of the dominant dip angle of the streamer seismic data. The calculation method is shown in formula (10):

[0148] Δθ=θ1-θ2 (10);

[0149] In formula (10), Δθ represents the difference between the first dip and the second dip of an imaging point, that is, the index difference of the structural attribute, θ1 represents the first dip calculated based on the streamer seismic data, and θ2 represents the second dip calculated based on the seafloor node seismic data.

[0150] After that, the index difference of the structural attribute corresponding to the imaging point is normalized to obtain the weight coefficient of the structural attribute corresponding to the imaging point, that is, the weight coefficient of the streamer seismic data related to the dip angle in the target fusion band. The specific calculation method is shown in formula (11):

[0151]

[0152] In formula (11), Δθ min The smallest indicator difference in the indicator gap data representing the constructed attribute, Δθ max Indicates the maximum indicator difference in the indicator gap data of the constructed attribute, w 1θ Represents the weight coefficient of the structural attribute corresponding to the imaging point.

[0153] Step S250, calculating first weighted data of the first seismic data according to the weight data of various seismic attributes, and calculating second weighted data of the second seismic data according to the first weighted data.

[0154] The weight data of each seismic attribute includes weight coefficients corresponding to multiple imaging points. The weight coefficients of various seismic attributes corresponding to each imaging point are added in equal proportion to obtain a first weight coefficient of the imaging point.

[0155] When the above three seismic attributes are used for seismic data fusion, the calculation method of the first weighting coefficient is shown in formula (12):

[0156] w1=w 1f / 3+w 1s / 3+w 1θ / 3 (12);

[0157] In formula (12), the numbers in the subscripts of each parameter represent the serial number of the fusion band, 1 is the serial number of the first fusion band, w1 represents the first weighting coefficient corresponding to the imaging point, and w 1f Represents the weight coefficient of the resolution attribute corresponding to the imaging point, w 1s Represents the weight coefficient of the imaging quality attribute corresponding to the imaging point, w 1θ Represents the weight coefficient of the structural attribute corresponding to the imaging point.

[0158] In an optional manner, in order to ensure energy conservation, the sum of the first weighting coefficient and the second weighting coefficient corresponding to the same imaging point is configured to be 1, and the calculation method of the second weighting coefficient corresponding to the imaging point is shown in formula (13):

[0159] w′1=1-w1 (13);

[0160] In formula (13), w1 represents the first weighting coefficient corresponding to the imaging point, and w′1 represents the second weighting coefficient corresponding to the imaging point.

[0161] For the target fusion frequency band, the above operation is repeated for each imaging point, and the dynamic weighting coefficient of the first seismic data and the second seismic data in the target fusion frequency band can be calculated; the above operation is repeated for each fusion frequency band, and the dynamic weighting coefficient of the two seismic data in each fusion frequency band can be calculated.

[0162] Fig.13 A cross-sectional diagram showing weight data associated with instantaneous frequency for first seismic data corresponding to a frequency band of 0-16 Hz, i.e., weight data for resolution attributes of a frequency band of 0-16 Hz corresponding to streamer seismic data; Fig.14 A cross-sectional diagram of the weight data related to the dip angle of the first seismic data corresponding to the 0-16 Hz frequency band is shown, i.e., the weight coefficient of the structural attribute of the 0-16 Hz frequency band corresponding to the streamer seismic data; Fig.15 A cross-sectional diagram of weight data related to the signal-to-noise ratio of the first seismic data corresponding to the 0-16 Hz frequency band is shown, i.e., the weight coefficient of the imaging quality attribute corresponding to the streamer seismic data; Fig.16 A cross-sectional diagram of multi-attribute constraint weighting coefficients of the second seismic data corresponding to the 0-16 Hz frequency band is shown, i.e., weighted data of the 0-16 Hz frequency band corresponding to the seafloor node seismic data; Fig.17 A cross-sectional diagram of multi-attribute constraint weighting coefficients of first seismic data corresponding to a frequency band of 0-16 Hz is shown, ie, weighted data of a frequency band of 0-16 Hz corresponding to streamer seismic data.

[0163] Step S260, performing weighted fusion processing on the first seismic data and its first weighted data, the second seismic data and its second weighted data, to obtain fused seismic data of a target fused frequency band.

[0164] Specifically, for the same imaging point, the seismic data corresponding to the imaging point in the first seismic data and the first weighting coefficient corresponding to the imaging point in the first weighted data are determined, and the seismic data corresponding to the imaging point in the second seismic data and the second weighting coefficient corresponding to the imaging point in the second weighted data are determined, and then the weighted sum is calculated based on the determined four data to obtain the seismic data fusion result of the imaging point. The seismic data fusion results of multiple imaging points constitute the fused seismic data of the target fusion frequency band.

[0165] Each fusion frequency band is determined as the target fusion frequency band in turn, and the fusion seismic data of each fusion frequency band is calculated according to the above method. Then, the fusion seismic data of different fusion frequency bands are added together, that is, the first seismic data and the second seismic data corresponding to multiple frequency bands are multiplied by the corresponding weighting coefficients and then added together to obtain the final result of the fusion of the streamer seismic data and the seafloor node seismic data. The calculation method is shown in formula (14):

[0166]

[0167] In formula (14), M(t) represents the final fusion result of the streamer seismic data and the seafloor node seismic data, i is the serial number of the fusion band, n is the total number of divided fusion bands, and C′ i (t) represents the streamer seismic data in the i-th fusion band, w i (t) represents the weighted data of the streamer seismic data in the i-th fusion band, O′ i (t) represents the data of the seafloor node seismic data in the i-th fusion frequency band, w′ i (t) represents the weighted data of the seafloor node seismic data in the i-th fusion frequency band.

[0168] Fig.18 shows the imaging profile of the streamer seismic data before fusion, Fig.19 The imaging profile of the seafloor node seismic data before fusion is shown. Fig. 20 The imaging profile of the fused seismic data is shown. Fig.18 The streamer seismic data before fusion and Fig.19 From the seafloor node seismic data before fusion, we can see Fig. 20 The shallow layer of the fused seismic data combines the high frequency advantage of the streamer seismic data and the high signal-to-noise ratio advantage of the seafloor node seismic data, while the mid-deep layer combines the high frequency advantage of the streamer seismic data and the imaging advantage of the bottom fuzzy zone of the seafloor node seismic data. Overall, compared with single seismic data, the fused seismic data has been greatly improved in terms of resolution, signal-to-noise ratio, structural details and fuzzy zone imaging.

[0169] In brief, the main steps of the method of the embodiment of the present application include: adaptively aligning the original streamer seismic data based on the seabed node seismic data, identifying the dominant frequency band to divide each fusion frequency band, calculating the weighting coefficient for each fusion frequency band, and finally dynamically weighted fusion of the seabed node seismic data and the streamer seismic data of multiple fusion frequency bands to obtain the final fusion result.

[0170] In summary, according to the seismic data fusion method provided in this embodiment, the DWT technology is first used to perform adaptive phase alignment to eliminate the inconsistency of formation dips caused by differences in multiple observation wave fields, and to eliminate the differences between the streamer seismic data and the seabed node seismic data caused by non-geological reasons, thereby laying a good data foundation for subsequent fusion processing; according to the imaging differences between the streamer seismic data and the seabed node seismic data in different frequency bands, different strata and different zones, multiple seismic attributes are used to identify the advantages of different seismic data, and dynamic weighting coefficients in the frequency domain are obtained, which can maximize the imaging advantages of the streamer seismic data and the seabed node seismic data and greatly improve the accuracy and effect of the fusion processing.

[0171] Fig.21 FIG. 1 is a schematic diagram showing the functional structure of the seismic data fusion device provided in the embodiment of the present application. Fig.21 As shown, the device comprises:

[0172] The frequency band division module 2101 is used to perform spectrum analysis on the streamer seismic data and the seafloor node seismic data respectively, and compare the spectrum analysis results to determine each fusion frequency band;

[0173] The difference processing module 2102 is used to extract the data in the target fusion frequency band from the streamer seismic data and the seafloor node seismic data respectively to obtain the first seismic data and the second seismic data; calculate the index difference data of various seismic attributes according to the first seismic data and the second seismic data; the target fusion frequency band is any fusion frequency band among the fusion frequency bands;

[0174] The weight processing module 2103 is used to determine the weight data of various seismic attributes according to the index gap data of various seismic attributes; calculate the first weighted data of the first seismic data according to the weight data of various seismic attributes, and calculate the second weighted data of the second seismic data according to the first weighted data;

[0175] The fusion module 2104 is used to perform weighted fusion processing on the first seismic data and its first weighted data, the second seismic data and its second weighted data, to obtain fused seismic data of a target fusion frequency band.

[0176] In an optional manner, the device further comprises:

[0177] The registration module is used to obtain the seabed node seismic data and the original streamer seismic data; based on the seabed node seismic data, the original streamer seismic data is subjected to phase registration processing to obtain the streamer seismic data.

[0178] In an optional manner, the index gap data of each seismic attribute includes the index difference value of the seismic attribute corresponding to each imaging point, and the weight data of each seismic attribute includes the weight coefficient of the seismic attribute corresponding to each imaging point;

[0179] The weight processing module 2103 is further used for:

[0180] For the index difference data of each seismic attribute, the index difference values ​​of each imaging point contained therein are normalized to obtain the weight coefficient of the seismic attribute corresponding to each imaging point.

[0181] In an optional manner, the first weighted data of the first seismic data includes a first weighting coefficient of each imaging point;

[0182] The weight processing module 2103 is further used for:

[0183] The weight coefficients of various seismic attributes corresponding to each imaging point are added in equal proportion to obtain the first weight coefficient of the imaging point.

[0184] In an optional manner, the various seismic attributes include at least two of resolution attributes, imaging quality attributes, and structural attributes.

[0185] In an optional manner, the difference processing module 2102 is further used to:

[0186] Calculating a first instantaneous frequency of each imaging point according to the first seismic data, and calculating a second instantaneous frequency of each imaging point according to the second seismic data;

[0187] The first instantaneous frequency and the second instantaneous frequency of each imaging point are subtracted to obtain the index difference value of the resolution attribute corresponding to each imaging point.

[0188] In an optional manner, the difference processing module 2102 is further used to:

[0189] Calculating a first signal-to-noise ratio of each imaging point according to the first seismic data, and calculating a second signal-to-noise ratio of each imaging point according to the second seismic data;

[0190] The first signal-to-noise ratio and the second signal-to-noise ratio of each imaging point are subtracted to obtain the index difference of the imaging quality attribute corresponding to each imaging point.

[0191] In an optional manner, the difference processing module 2102 is further used to:

[0192] Calculating a first dip angle of each imaging point according to the first seismic data, and calculating a second dip angle of each imaging point according to the second seismic data;

[0193] The first inclination angle and the second inclination angle of each imaging point are subtracted to obtain the index difference of the structural attribute corresponding to each imaging point.

[0194] To summarize, according to the seismic data fusion device provided in this embodiment, the fusion frequency band is divided according to the imaging difference between the towed cable seismic data and the seabed node seismic data, the seismic data is fused according to the fusion frequency band, and the advantages of different seismic data are identified by using multiple seismic attributes to obtain dynamic weighting coefficients in the frequency domain. This can fully utilize the advantages of seismic data in different frequency bands and different attributes, thereby maximizing the imaging advantages of the towed cable seismic data and the seabed node seismic data, and improving the seismic data fusion effect.

[0195] An embodiment of the present application provides a non-volatile computer storage medium, which stores at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the seismic data fusion method in any of the above method embodiments.

[0196] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the seismic data fusion method in any of the above method embodiments.

[0197] Fig. 22 A schematic diagram of the structure of a computing device provided in an embodiment of the present application is shown, and the specific embodiment of the present application does not limit the specific implementation of the computing device.

[0198] like Fig. 22 As shown, the computing device may include: a processor (processor) 2202 , a communications interface (Communications Interface) 2204 , a memory (memory) 2206 , and a communication bus 2208 .

[0199] The processor 2202, the communication interface 2204, and the memory 2206 communicate with each other via a communication bus 2208. The communication interface 2204 is used to communicate with other devices such as a client or other server network elements. The processor 2202 is used to execute a program 2210, which can specifically execute the relevant steps in the above-mentioned embodiment of the seismic data fusion method for a computing device.

[0200] Specifically, the program 2210 may include program code, which includes computer operation instructions.

[0201] The processor 2202 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0202] The memory 2206 is used to store the program 2210. The memory 2206 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0203] The program 2210 can be specifically used to enable the processor 2202 to execute the seismic data fusion method in any of the above method embodiments. The specific implementation of each step in the program 2210 can refer to the corresponding description in the corresponding steps and units in the seismic data fusion embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above-mentioned method embodiments, which will not be repeated here.

[0204] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0205] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0206] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present application.

[0207] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0208] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0209] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components according to the embodiments of the present application. The application can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0210] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A seismic data fusion method, characterized in that: include: Conduct spectrum analysis on the streamer seismic data and the seafloor node seismic data respectively, and compare the spectrum analysis results to determine each fusion frequency band; Respectively extracting data within a target fusion frequency band from the streamer seismic data and the seafloor node seismic data to obtain first seismic data and second seismic data; According to the first seismic data and the second seismic data, the index gap data of various seismic attributes are calculated; the target fusion frequency band is any fusion frequency band among the fusion frequency bands; According to the index gap data of various seismic attributes, the weight data of various seismic attributes are determined; Calculating first weighted data of the first seismic data according to the weight data of the various seismic attributes, and calculating second weighted data of the second seismic data according to the first weighted data; A weighted fusion process is performed on the first seismic data and its first weighted data, and the second seismic data and its second weighted data to obtain fused seismic data of the target fused frequency band.

2. The seismic data fusion method according to claim 1, characterized in that: The method further comprises: Acquire seafloor node seismic data and raw streamer seismic data; The original streamer seismic data is subjected to phase registration processing based on the seafloor node seismic data to obtain the streamer seismic data.

3. The seismic data fusion method according to claim 1 or 2, characterized in that: The index difference data of each seismic attribute includes the index difference value of the seismic attribute corresponding to each imaging point, and the weight data of each seismic attribute includes the weight coefficient of the seismic attribute corresponding to each imaging point; Determining weight data of various seismic attributes according to the index gap data of various seismic attributes further includes: For the index difference data of each seismic attribute, the index difference values ​​of each imaging point contained therein are normalized to obtain the weight coefficient of the seismic attribute corresponding to each imaging point.

4. The seismic data fusion method according to claim 3, characterized in that: The first weighted data of the first seismic data includes a first weighting coefficient of each imaging point; The step of calculating the first weighted data of the first seismic data according to the weight data of the various seismic attributes further comprises: The weight coefficients of various seismic attributes corresponding to each imaging point are added in equal proportion to obtain the first weight coefficient of the imaging point.

5. The seismic data fusion method according to any one of claims 1 to 4, characterized in that: The various seismic attributes include at least two of a resolution attribute, an imaging quality attribute, and a structural attribute.

6. The seismic data fusion method according to claim 5, characterized in that: The step of calculating the index gap data of various seismic attributes based on the first seismic data of the streamer seismic data within the target fusion frequency band and the second seismic data of the seafloor node seismic data within the target fusion frequency band further comprises: Calculating a first instantaneous frequency of each imaging point according to the first seismic data, and calculating a second instantaneous frequency of each imaging point according to the second seismic data; The first instantaneous frequency and the second instantaneous frequency of each imaging point are subtracted to obtain the index difference value of the resolution attribute corresponding to each imaging point.

7. The seismic data fusion method according to claim 5, characterized in that: The step of calculating the index gap data of various seismic attributes based on the first seismic data of the streamer seismic data within the target fusion frequency band and the second seismic data of the seafloor node seismic data within the target fusion frequency band further comprises: Calculating a first signal-to-noise ratio of each imaging point according to the first seismic data, and calculating a second signal-to-noise ratio of each imaging point according to the second seismic data; The first signal-to-noise ratio and the second signal-to-noise ratio of each imaging point are subtracted to obtain the index difference of the imaging quality attribute corresponding to each imaging point.

8. The seismic data fusion method according to claim 5, characterized in that: The step of calculating the index gap data of various seismic attributes based on the first seismic data of the streamer seismic data within the target fusion frequency band and the second seismic data of the seafloor node seismic data within the target fusion frequency band further comprises: Calculating a first dip angle of each imaging point according to the first seismic data, and calculating a second dip angle of each imaging point according to the second seismic data; The first inclination angle and the second inclination angle of each imaging point are subtracted to obtain the index difference of the structural attribute corresponding to each imaging point.

9. A seismic data fusion device, characterized in that: include: The frequency band division module is used to perform spectrum analysis on the streamer seismic data and the seafloor node seismic data respectively, and compare the spectrum analysis results to determine each fusion frequency band; A difference processing module, used to extract data within a target fusion frequency band from the streamer seismic data and the seafloor node seismic data, respectively, to obtain first seismic data and second seismic data; According to the first seismic data and the second seismic data, the index gap data of various seismic attributes are calculated; the target fusion frequency band is any fusion frequency band among the fusion frequency bands; A weight processing module, used to determine weight data of various seismic attributes according to the index gap data of various seismic attributes; calculate first weighted data of the first seismic data according to the weight data of the various seismic attributes, and calculate second weighted data of the second seismic data according to the first weighted data; A fusion module is used to perform weighted fusion processing on the first seismic data and its first weighted data, the second seismic data and its second weighted data, to obtain fused seismic data of the target fusion frequency band.

10. A computing device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the seismic data fusion method according to any one of claims 1-8.

11. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the seismic data fusion method according to any one of claims 1-8.

12. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the seismic data fusion method according to any one of claims 1 to 8.

Images